The key challenges in addressing the problems lie in (1) integrating theoretical knowledge about neural networks to define utilization-based class encodings, (2) computing class encodings within an allocated time (e.g., 15 min per AI model), with limited computational resources and over hundreds of thousands of training images, and (3) visualizing and interpreting class encoding patterns for a variety of AI model architectures at the granularity of AI model graphs, subgraphs, and tensor-states.
All three challenges are intensified by increasing sizes of training datasets and by advancing complexities of AI architectures as enumerated with examples in Table REF . As of today, AI architectures are
(a) very complex in terms of the number of parameters (from 60K parameters in LeNet model [1]}, to common networks having millions and billions of parameters, such as 160 billion reported by Trask et al. [2]}, and bleeding-edge networks with trillion-parameters in AI language models [3]}),
(b) very heterogeneous in terms of types of computation units in computational AI graphs, and
(c) high dimensional in terms of data tensors generated by AI graph computation units.
<TABLE>